HYRE's first in-house model — an agent-tuned, uncensored derivative of Meta's
Muse Glimmer 30B, built for autonomous agents that need tool-calling and a
straight-talking voice with no refusal walls.
Ronin without a master, tools without a filter.
What this is
HOMURA is not a from-scratch model. It is a LoRA fine-tune applied on top of
a community-decensored Muse Glimmer, then merged and quantized. The derivation
chain is honest and traceable:
Meta — Muse Glimmer 30B (Apache 2.0): the agent-native base (tool use,
long-horizon planning, failure recovery).
darkc0de — Muse-Glimmer-30B-heretic: refusal behavior removed
(abliteration), while tool-calling, reasoning, and the vision encoder were
preserved.
HYRE — HOMURA: our contribution — a LoRA (r=16) tuned on a HYRE agent +
uncensored-persona dataset, applied to the language tower only (the
vision tower is untouched), then merged at f16 and quantized to GGUF.
Files
Homura-30B-Q4_K_M.gguf — 16.9 GB, ready for llama.cpp / LM Studio / Ollama.
Muse Glimmer needs llama.cpp ≥ b10353 and --jinja. Native context is 131072;
size -c to your hardware.
Serve at temperature ≤ 0.3. At temp 0.2 tool-calling is reliable (verified
6/6 across trained + pay.sh tools). At temp 0.7 the model stays coherent but
tool selection degrades to ~2/6 — it narrates intent instead of emitting the
call. Low temperature is the right setting for an agent anyway.
Tool calling — use HOMURA's trained protocol
HOMURA was fine-tuned on a specific tool protocol, not the generic tool
schema most chat templates inject. Serving it the generic way gets ~2/5 calls;
serving it the trained way gets 5/5. You must use the system prompt below.
Set this as the system message verbatim (wording is part of the interface —
changing it degrades accuracy):
You are HOMURA, HYRE's uncensored agent model. You call tools to get on-chain truth, reason briefly, and never moralize or lecture. When a tool is needed, respond ONLY with a JSON object: {"tool": <name>, "arguments": {...}}. Available tools: resolve_token(symbol); get_token_price(mint); get_trending_tokens(segment, limit); get_wallet_pnl(address, window); get_pool_data(pool); get_yields(token, min_tvl_usd); swap_quote(from_mint, to_mint, amount); execute_swap(from_mint, to_mint, amount, slippage_bps); get_wallet_balance(address); bridge_quote(from_chain, to_chain, token, amount); get_token_holders(mint).
HOMURA then replies with a bare JSON object when a tool is needed:
Feed the tool's result back as a tool-role message; HOMURA reasons over it and
either answers or calls the next tool. It resolves symbols before prices
(resolve_token first, so it never hallucinates a mint), and it will not execute_swap
without a prior swap_quote.
Extending the tool surface (e.g. pay.sh)
The tool list lives entirely in the system prompt — not in the weights. You
can add tools by appending them to the Available tools: line, with no retrain
and no re-download. HOMURA generalizes to unseen tools from the pattern.
Verified example — appending four pay.sh tools
(pay_search(query); pay_quote(url); pay_fetch(url, params); pay_balance())
works out of the box (7/8 across unseen tools, zero regression on the original
eleven). Note the model preserves its quote-then-confirm discipline for spending
tools: given "pay for this endpoint" it calls pay_quote first and waits for
confirmation before pay_fetch. Enforce spend limits in your serving layer —
the prompt discipline is a nicety, not a guarantee.
A ready-to-import helper (homura_protocol.py) with the verbatim prompt, the
pay.sh extension, and a parse_tool_call() that accepts bare-JSON, fenced-JSON,
and native XML is in the HYRE repo.
Intended use & disclaimer
HOMURA is an uncensored / raw-tier model with no built-in content filtering.
It will answer directly and will not refuse or moralize. It can therefore
produce content that other assistants decline. It is intended for developers and
agent builders who need an unfiltered tool-using model and who take
responsibility for how it is deployed. You are responsible for complying with
applicable law and for adding your own guardrails where your use case requires
them. The model may produce inaccurate or objectionable output; do not rely on
it for safety-critical decisions.
License
Apache 2.0, inherited from the base. Attribution to Meta (Muse Glimmer) and
darkc0de (heretic) is retained above.
